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Updated: Jan 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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一个以稳定性为导向的生物标志物选择框架,由强大的等级聚合和L1-稀疏模型协同驱动

Jigen Luo1,2, Jianqiang Du3, Jia He1,2

  • 1School of Intelligent Medicine and Information Engineering, Jiangxi University of Chinese Medicine, Nanchang 330004, China.

Metabolites
|December 24, 2025
PubMed
概括

本研究介绍了FRL-TSFS,这是一个用于omics数据的新型特征选择框架. 它通过提高选定特征的稳定性和可重复性来增强生物标志物发现,这对于代谢学和基因表达研究至关重要.

关键词:
在L1-sparse建模中.生物标志物选择,生物标志物选择特性选择稳定性 特性选择稳定性代谢生物组的代谢生物组强大的等级聚合.面向稳定性的特征选择框架.

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